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Running Cell Ranger count

Cell Ranger's pipelines analyze sequencing data produced from Chromium Single Cell Gene Expression. It also processes data generated by using Feature Barcode technology. The count pipeline cannot be used to analyze Fixed RNA Profiling (FRP) data.

You can run 10x Genomics single cell pipelines with 10x Genomics Cloud Analysis, our recommended method to easily process FASTQ files into Cell Ranger output files for most new customers. Cloud Analysis is currently available only in the United States and Canada. Click here to sign up for a free account.
New in Cell Ranger v7.0: Intronic reads are counted by default for whole transcriptome gene expression data. For more information, see our recommendation on including introns for gene expression analysis page.

The analysis involves the following steps:

  1. Run cellranger mkfastq, bcl-convert, or bcl2fastq on the Illumina BCL output folder to demultiplex and generate FASTQ files.

  2. Run cellranger count on each GEM well that was demultiplexed. If you created a Feature Barcode library alongside the Gene Expression library, you will pass them both to cellranger count at this point. See Feature Barcode Analysis for details.

  3. Optionally, run cellranger aggr to aggregate multiple GEM wells from a single experiment that were analyzed by cellranger count.

  4. Optionally, run cellranger reanalyze to rerun the secondary analysis on a library or aggregated set of libraries (i.e., PCA, t-SNE, and clustering) and fine-tune parameters.

For the following example, assume the Illumina BCL output is in a folder named /sequencing/140101_D00123_0111_AHAWT7ADXX.

First, generate FASTQ files. For example, if the flow cell ID was HAWT7ADXX and you use cellranger mkfastq to demultiplex, the output FASTQ files will be in HAWT7ADXX/outs/fastq_path.

If you are already starting with FASTQ files, you can skip this step and proceed directly to run cellranger count.

To generate single cell feature counts for a single library, run cellranger count with the following arguments. For a complete listing of the arguments accepted, see the Command Line Argument Reference below, or run cellranger count --help. Cell Ranger must not be used for Single Cell Multiome Analysis. For Single Cell Multiome ATAC + Gene Expression libraries, use Cell Ranger ARC.

Starting in Cell Ranger 7.0, the expected number of cells can either be auto-estimated or specified with --expect-cells (e.g., to replicate a previous analysis). If needed, automated cell calling can be overridden with the --force-cells option. See Gene Expression algorithm overview for details.
For help on which arguments to use to target a particular set of FASTQs, consult Specifying Input FASTQ Files for 10x Genomics pipelines.

After determining these input arguments and customizing the code, run cellranger:

cd /home/jdoe/runs cellranger count --id=sample345 \ --transcriptome=/opt/refdata-gex-GRCh38-2020-A \ --fastqs=/home/jdoe/runs/HAWT7ADXX/outs/fastq_path \ --sample=mysample \ --localcores=8 \ --localmem=64

Following a series of checks to validate input arguments, cellranger count pipeline stages will begin to run:

Martian Runtime - v4.0.8 Running preflight checks (please wait)... Checking sample info... Checking FASTQ folder... Checking reference... Checking optional arguments... ...

By default, Cell Ranger will use all of the cores available on your system to execute pipeline stages. You can specify a different number of cores to use with the --localcores option; for example, --localcores=16 will limit Cell Ranger to using up to sixteen cores at once. Similarly, --localmem will restrict the amount of memory (in GB) used by Cell Ranger.

The pipeline will create a new folder named with the sample ID you specified (e.g. /home/jdoe/runs/sample345) for its output. If this folder already exists, Cell Ranger will assume it is an existing pipestance and attempt to resume running it.

A successful cellranger count run should conclude with a message similar to this:

Outputs: - Run summary HTML: /opt/sample345/outs/web_summary.html - Run summary CSV: /opt/sample345/outs/metrics_summary.csv - BAM: /opt/sample345/outs/possorted_genome_bam.bam - BAM index: /opt/sample345/outs/possorted_genome_bam.bam.bai - Filtered feature-barcode matrices MEX: /opt/sample345/outs/filtered_feature_bc_matrix - Filtered feature-barcode matrices HDF5: /opt/sample345/outs/filtered_feature_bc_matrix.h5 - Unfiltered feature-barcode matrices MEX: /opt/sample345/outs/raw_feature_bc_matrix - Unfiltered feature-barcode matrices HDF5: /opt/sample345/outs/raw_feature_bc_matrix.h5 - Secondary analysis output CSV: /opt/sample345/outs/analysis - Per-molecule read information: /opt/sample345/outs/molecule_info.h5 - CRISPR-specific analysis: null - Loupe Browser file: /opt/sample345/outs/cloupe.cloupe - Feature Reference: null - Target Panel File: null Waiting 6 seconds for UI to do final refresh. Pipestance completed successfully! yyyy-mm-dd hh:mm:ss Shutting down. Saving pipestance info to "tiny/tiny.mri.tgz"

The output of the pipeline will be contained in a folder named with the sample ID you specified (e.g. sample345).

Once cellranger count has successfully completed, you can browse the resulting web summary HTML file in any supported web browser and open the .cloupe file in Loupe Browser. Refer to the Understanding Outputs 3' Gene Expression Outputs page for descriptions about all output files.

A list of all Cell Ranger count arguments are provided on the Cell Ranger manual page.